title: Relationship between nursing documentation and patients' mortality.
abstract: BACKGROUND
  Nurses alter their monitoring behavior as a patient's clinical condition deteriorates, often detecting and documenting subtle changes before physiological trends are apparent. It was hypothesized that a nurse's behavior of recording optional documentation (beyond what is required) reflects concern about a patient's status and that mining data from patients' electronic health records for the presence of these features could help predict patients' mortality.
  
  
  METHODS
  Data-mining methods were used to analyze electronic nursing documentation from a 15-month period at a large, urban academic medical center. Mortality rates and the frequency of vital sign measurements (beyond required) and optional nursing comment documentation were analyzed for a random set of patients and patients who experienced a cardiac arrest during their hospitalization. Patients were stratified by age-adjusted Charlson comorbidity index.
  
  
  RESULTS
  A total of 15,000 acute care patients and 145 cardiac arrest patients were studied. Patients who died had a mean of 0.9 to 1.5 more optional comments and 6.1 to 10 more vital signs documented within 48 hours than did patients who survived. A higher frequency of comment and vital sign documentation was also associated with a higher likelihood of cardiac arrest. Of patients who had a cardiac arrest, those with more documented comments were more likely to die.
  
  
  CONCLUSIONS
  For the first time, nursing documentation patterns have been linked to patients' mortality. Findings were consistent with the hypothesis that some features of nursing documentation within electronic health records can be used to predict mortality. With future work, these associations could be used in real time to establish a threshold of concern indicating a risk for deterioration in a patient's condition.
authors.affiliation: Partners Healthcare Systems, Wellesley, MA 02481, USA. sacollins@partners.org
authors.forename: Sarah A
authors.initials: SA
authors.lastname: Collins
authors.forename: Kenrick
authors.initials: K
authors.lastname: Cato
authors.forename: David
authors.initials: D
authors.lastname: Albers
authors.forename: Karen
authors.initials: K
authors.lastname: Scott
authors.forename: Peter D
authors.initials: PD
authors.lastname: Stetson
authors.forename: Suzanne
authors.initials: S
authors.lastname: Bakken
authors.forename: David K
authors.initials: DK
authors.lastname: Vawdrey
mesh_terms: D000368:Aged; D057225:Data Mining; D004282:Documentation / Q000706:statistics & numerical data*; D057286:Electronic Health Records; D005260:Female; D017052:Hospital Mortality; D006801:Humans; D007902:Length of Stay; D008297:Male; D008875:Middle Aged; D008991:Monitoring, Physiologic / Q000706:statistics & numerical data*; D009741:Nursing Staff, Hospital; D062072:Patient Acuity; D055986:Vital Signs